Infrared Spectroscopy Technology Research Articles

Real-time grading of roasted tobacco using near infrared spectroscopy technology

Traditional tobacco quality grading methods have limitations such as high human resource consumption and poor consistency, resulting in the grading accuracy being affected. Near infrared spectroscopy (NIRS) technology, as a rapid and non-destructive analytical method, has been widely used for online inspection. This study aims to accomplish rapid, accurate and reliable real-time grading of tobacco quality by employing online near-infrared spectroscopy to detect key chemical indicators. A key indicators selection method based on information gain and Fisher criterion (IFRI) was proposed in this paper by associating tobacco chemical indicators with traditional quality grades. The key chemical indicators were selected from many indicators by two classical feature selection functions, and a chemical indicator grading table was designed based on a large number of statistical data on tobacco chemical indicators, and the table was applied to grade the tobacco quality of real data from different years. The results show that the IFRI algorithm is an effective method for selecting key indicators, and the selected reducing sugars-to-total nitrogen ratio and nicotine are the key indicators for tobacco quality grading. Moreover, we demonstrated that the designed grading table is reasonable and feasible. Compared with traditional grading, chemical indicator grading based on near infrared spectroscopy can not only be realized quickly by using an online near infrared instrument, but also the tobacco of the same grade is less discrete, better consistency and more representative after grading, and it is applicable to tobacco samples of different years, which is a convenient, rapid and promising grading method that can provide a reference for tobacco quality grading. Furthermore, the formulation design by utilizing different grades after grading can greatly improve the utilization rate of tobacco leaves. Notably, the method can also be applied to other fields, such as quality control of agricultural products, monitoring of food processing processes, etc.

Low temperature catalytic activity of molybdenum promoted two-dimensional boron nitride in propane oxidative dehydrogenation reaction

The catalytic oxidation dehydrogenation of propane to propylene, achieved at low temperatures and with high selectivity on catalysts supported by non-noble metals, exhibits significant potential for a wide range of applications. The performance of boron-based catalysts in ODHP is remarkable; however, there is a dearth of research on their low-temperature range performance. The present study employed a straightforward impregnation method to synthesize a range of Mo/h-BN-γ-Al2O3 catalysts with varying molybdenum loadings, focusing on the comparison between commercial h-BN and h-BN-γ-Al2O3 mixed support. The Mo/h-BN-γ-Al2O3 catalyst demonstrates remarkable low-temperature ODHP activity, with an initial conversion rate of 2.36 % and a propylene selectivity of 96 % at a reaction temperature of 260 ℃, exhibiting good stability over 50 h, the catalytic performance of Mo/h-BN-γ-Al2O3 in the low-temperature range surpasses that of commercial h-BN by a significant margin. Comparative experiments, along with physical and chemical characterization techniques, demonstrate that the incorporation of an appropriate quantity of Mo enhances the catalyst's acidity and redox capability. Furthermore, the synergistic effect between MoOx and BOx promotes low-temperature ODHP. The mechanism underlying the catalyst's low-temperature reaction was investigated using in-situ infrared spectroscopy technology.

The cocrystals of 3,4-dihydroxyphenylactic acid: Improved phase stability and reduced hygroscopicity

Cocrystal technology is a collaborative strategy for improving the physicochemical properties of active pharmaceutical ingredients. To overcome the obstacles of phase instability and high hygroscopicity of 3,4-dihydroxyphenylactic acid (DHPAA), three new cocrystals of DHPAA with nicotinamide (NIC), caprolactam (CPR) and sarcosine (SAR) were designed and synthesized for the first time, which were characterized using single crystal X-ray diffraction, powder X-ray diffraction, differential scanning calorimetry, thermogravimetric analysis and infrared spectroscopy technologies. The results of stability test and hygroscopicity study demonstrated that DHPAA−NIC, DHPAA−CPR and DHPAA−SAR exhibited a remarkable improvement in the phase stability of DHPAA, meanwhile, DHPAA−NIC and DHPAA−CPR also displayed reduced hygroscopicity compared to DHPAA. This study can provide new directions of improving the druggability of DHPAA through cocrystallization.

Research Progress of Infrared Spectroscopy Technology Enhanced by Polaritons in Two-Dimensional Materials (Invited)

Research Progress of Infrared Spectroscopy Technology Enhanced by Polaritons in Two-Dimensional Materials (Invited)

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease.

Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people's daily diet. China has always been a big apple consumer, and with the improvement of people's life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease.

The research on the molecular spectroscopic recognition mechanism of microplastics in typical agricultural media

To study the identification mechanism of microplastics in agricultural environmental media by molecular spectroscopy, two typical media, soil and fishmeal, were selected for this study. Three common microplastics, PE, PP, and PS, were used as research objects. Near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometric methods were used to explore microplastics' identification and analysis effects in different agricultural media and reveal their identification mechanisms. PCA analysis revealed that different soil types would affect the identification results of microplastics. PLS-DA discriminant analysis showed that the accuracy of NIR spectroscopy technology in identifying microplastics in different types of soil decreased in the order of sand > loam > clay. In contrast, the rule of MIR spectroscopy technology was the opposite. The sensitivity and specificity of the three microplastics in the NIR model of sand and the infrared spectroscopy model of fishmeal reached 1.000. NIR spectroscopy technology is suitable for identifying microplastics in soil, while infrared spectroscopy technology is more suitable for identifying microplastics in fish meal. Furthermore, based on the VIP values of each wavelength point in the spectrum, the characteristic bands that have essential contributions to identifying microplastics in soil were screened out. The NIR spectra of 4500–4300 cm−1, 4300–3900 cm−1 and 7100–5800 cm−1 are the most essential characteristic bands for identifying microplastics in clay, loam, and sand, respectively. The MIR of 3000–2900 cm−1 and 700–650 cm−1 were the most essential characteristic bands for identifying microplastics in soils, and the overlap of the characteristic spectra of the three soils reached 59.45%. The NIR spectra of 6050–5600 cm−1, 4700–4000 cm−1 and the MIR spectra of 2300–1900 cm−1 and 800–400 cm−1 are the most essential characteristic bands for the identifying microplastics in fishmeal. This study provides a more suitable technical "solution" for identifying microplastics in environmental media, which is of great significance for improving the accuracy of molecular spectroscopic identification in environmental media and clarifying the interaction between microplastics and environmental media.

Habit-DisHabit Design with a Quadratic Equation: A Better Model of the Hemodynamic Changes in Preschoolers during the Dimension Change Card Sorting Task.

General linear modeling (GLM) has been widely employed to estimate the hemodynamic changes observed by functional near infrared spectroscopy (fNIRS) technology, which are found to be nonlinear rather than linear, however. Therefore, GLM might not be appropriate for modeling the hemodynamic changes evoked by cognitive processing in developmental neurocognitive studies. There is an urgent need to identify a better statistical model to fit into the nonlinear fNIRS data. This study addressed this need by developing a quadratic equation model to reanalyze the existing fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected from the mixed-order design Dimensional Change Card Sort (DCCS) task and verified the model with a new set of data with the Habit-DisHabit design. First, comparing the quadratic and cubic modeling results of the mixed-order design data indicated that the proposed quadratic equation was better than GLM and cubic regression to model the oxygenated hemoglobin (HbO) changes in this task. Second, applying this quadratic model with the Habit-DisHabit design data verified its suitability and indicated that the new design was more effective in identifying the neural correlates of cognitive shifting than the mixed-order design. These findings jointly indicate that Habit-DisHabit Design with a quadratic equation might better model the hemodynamic changes in preschoolers during the DCCS task.

Study on marine microplastics monitoring based on infrared spectroscopy technology

In recent years, microplastics particles have been detected in many sea areas around the world. Microplastics has done great harm to marine and terrestrial seawater ecosystems, so it is necessary to obtain the effective statistical data of microplastics in the environment accurately and quickly for the further study of pollution in microplastics. In this paper, based on IR (Infrared Spectroscopy) technology, hyperspectral images of marine microplastics samples containing different materials were obtained. SVM (Support Vector Machine) algorithm is used to identify microplastics in hyperspectral images. The results show that the microplastics abundance ranges from 5.193 to 20.281 N/L, 6.087 to 38.679 N/L and 7.498 to 11.084 N/L, respectively, and the average abundance is 11.83 N/L, 24.84 N/L and 19.27 N/L, respectively. The types of microplastics in the bottom water of the bay in the study area are mainly fibers (53–68%) and debris (23–34%). NIR (Near Infrared) analysis shows that the characteristic curves of microplastics spectra of the same species with different particle sizes are different. IR technology combined with chemometrics algorithm has great potential for the detection of microplastics in seawater surface and seawater. This method is simple and feasible, and has the feasibility of popularization.

Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators.

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp=0.9376) and P/F ratio (Rp=0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.

Infrared spectroscopy for ageing assessment of insulating oils via dielectric loss factor and interfacial tension

Abstract Along with the increase in demand for electricity, dependability requirements for the power supply system also increase. Transformers are the backbone of the electrical system; thus, they must function reliably and safely. In this study, a method based on Fourier-transform infrared spectroscopy technology is proposed for assessing the ageing level of insulating oils. Copper-catalysed thermal ageing is applied to four distinct insulating oils at a temperature of 12 0 ° C 12{0}^{^\circ }{\rm{C}} . The insulating oil ageing peaks are promptly measured using infrared spectroscopy, and the peak area ratios are associated with the interfacial tension and dielectric loss factor. It is observed that the peak area ratio of the ageing peaks and interfacial tension are significantly correlated, and this relationship can be used to determine the ageing condition of the insulating oil. The findings of this study can be used to design sensors for online monitoring of insulating oil quality.

Self-expansion full information optimization strategy: Convenient and efficient method for near infrared spectrum auto-analysis

An essential step in the application of near infrared spectroscopy technology is the spectrum preprocessing. A reasonable implementation of it ensures that the effective spectral information is correctly extracted and, also that the model's accuracy is increased. However, some analysts' research still uses the manual approach of trial and error, particularly those less skilled ones. Previous papers have provided preprocessing optimization algorithms for NIR, but there are still some problems that need to be resolved, such as the unwieldy sequence determination of preprocessing method or, the fluctuated optimization outcomes or, lack of sufficient statistical information. This research suggests a spectrum auto-analysis methodology named self-expansion full information optimization strategy, a new powerful open-source technique for concurrently addressing all of these above issues simultaneously. For the first time in the field of chemometrics, this algorithm offers a reliable and effective automatic near infrared auto-modelling method based on the statistical informatics. With the aid of its built-in modules, such as information generators, spectrum processors, etc., it is able to fully search the common preprocessing techniques, which is determined by Monte Carlo cross validation. Then the final ensemble calibration model is built by employing the optimized preprocessing schemes, along with the wavelength variables screening algorithm. The optimization strategy can offer the user objective useful statistics information created throughout the modeling process to further examine the model's effectiveness. The results demonstrate that the suggested method can easily and successfully extract spectrum information and develop calibration models by putting it to the test on two groups of actual near-infrared spectral data. Additionally, this optimization strategy can also be applied to other spectrum analysis areas, such Raman spectroscopy or infrared spectroscopy, by changing a few of its parameters, and has extraordinary application value.

Review Of Near Infrared Spectrum Detection Based on Deep Learning

Near infrared spectroscopy technology has the characteristics of accurate analysis of material composition and characteristics, quantitative testing, non-destructive testing, and so on. With the progress of artificial intelligence and the development of deep learning technology, near-infrared spectroscopy detection systems based on deep learning algorithms have emerged, which include various typical methods and have achieved good application results in food safety monitoring, pollutant detection, and drug analysis. This article provides a systematic review of domestic and foreign literature, introducing the principles, characteristics, and development history of deep learning and near-infrared spectroscopy technology. It discusses the research significance of near-infrared spectroscopy detection based on deep learning. It also reviews the latest progress in deep learning based near-infrared spectroscopy related technologies, elaborates on the advantages, disadvantages, and applicable fields of the methods, and makes prospects and predictions for the future development trends in this field.

Quantitative model of near infrared spectroscopy based on pretreatment combined with parallel convolution neural network

With the advancement of near infrared spectroscopy and deep learning technology, nondestructive quantitative testing plays a crucial role in various fields such as agriculture, petrochemicals, medicine, food, and forage. Currently, a high precision prediction model combined with spectral pretreatment is gaining popularity. In this paper, a quantitative analysis model of convolutional neural network including parallel network module (PaBATunNet) was proposed. PaBATunNet was composed of a one-dimensional convolutional layer, a parallel convolution module, a flattening layer, four fully connected layers and a parameter regulator (PR). The parallel convolution module was made up of five submodules and a Concatenate function. The linear and nonlinear multidimensional features of the spectra were extracted by five submodules and spliced by Concatenate function. The prediction accuracy of PaBATunNet was improved by optimizing the model parameters through the PR. Moreover, eight spectral pretreatment methods combined with PaBATunNet were tested on public datasets of beer, milk, and grain. The results indicated that the prediction accuracy of PaBATunNet with different spectral pretreatment increased by 4.83% to 28.40% on beer, 7.09% to 27.99% on milk and 4.96% to 25.31% on grain, compared to the PaBATunNet with original spectral. Among all models, the first derivative (D1) PaBATunNet (D1-PaBATunNet) performed the best. Compared with D1 partial least squares (D1-PLS), the prediction accuracy of D1-PaBATunNet increased by 34.89%, 65.04%, and 48.26% on beer, milk, and grain, respectively. Compared with D1 principal component regression (D1-PCR), the prediction accuracy increased by 34.17%, 63.98%, and 48.08% on beer, milk, and grain, respectively. Compared with D1 support vector machine (D1-SVM), the prediction accuracy increased by 39.29%, 61.78%, and 50.50% on beer, milk, and grain, respectively. Compared with D1 back propagation neural network (D1-BP), the prediction accuracy increased by 90.29%, 63.72%, and 44.75% on beer, milk, and grain, respectively. The problems of low prediction accuracy, poor stability, poor generalization ability and high risk of overfitting have been solved by D1-PaBATunNet. This study establishes an essential theoretical foundation for building a fast, nondestructive and high-precision quantitative analysis model of near infrared spectroscopy.

Intelligent component detection of quaternary blended oil based on near infrared spectroscopy technology

AbstractIn this study, near infrared spectroscopy (NIRS) technique was used for quantitative detection of quaternary blended oil. After a series of preprocessing, the prediction effects of the three models and their preprocessing combinations were compared. Taking soybean oil content prediction as an example, random forest (RF) model had better performance after second derivative (D2) optimization. In feature selection, a two‐step feature selection method was adopted to extract the feature wavelength. First, the elastic net (EN) was used for the initial screening of feature wavelengths, and most irrelevant features were eliminated. The number of feature wavelengths was reduced from 1048 to 134. After that, the competitive adaptive re‐weighted sampling (CARS) method was used to screen the remaining characteristic wavelengths more carefully, and 20 effective characteristic wavelengths were selected. Finally, a quantitative detection model was established based on 20 effective characteristic wavelengths selected by EN + CARS. Evaluated by the test set, The correlation coefficient of determination (R2), root‐mean‐square error of prediction (RMSEP) and Relative Percent Difference (RPD) values of 2D + EN + CARS + RF model were 0.97953, 1.34306 and 7.08875, respectively. The results showed that the two‐step feature selection method can effectively extract the feature wavelength, and the NIRS technology can realize the intelligent detection of blended oil components.

Sex classification of silkworm pupae from different varieties by near infrared spectroscopy combined with chemometrics

The accuracy of sex classification of silkworm pupae is directly related to the quality of crossbred silkworm pupae and silkworm silk. Manual selection as the traditional method for sex identification of silkworm pupae is labor-intensive and time-consuming. In addition, there are hundreds of silkworm pupae varieties. Currently, the widely used near infrared spectroscopy（NIR) technology showed unsatisfactory accuracy in distinguishing the sex of different species of silkworm pupae. Thus, we developed a method combining NIR and contrastive principal component analysis(cPCA) to achieve high precision sex classification of different silkworm pupae by introducing the background data set to eliminate the influence of silkworm pupae varieties. The experimental results showed that the cPCA analysis showed better performance (98.34%) compared with the accuracy of PCA analysis (71.36%). After characteristic wavenumber selection by iPLS, the accuracy could be improved to 99.58% by using the cPCA algorithm. The combination of NIR spectroscopy with cPCA provides a method for developing portable devices to quickly identify the sex of silkworm pupae.

Kano, Kayak ve Kürek Performanslarının Kas Oksijen Doygunluğuna Etkisi: Deneysel Çalışma

Objective: The aim of this study is to compare the intramuscular oxygen saturation results during the short and long distance performances of canoe, kayak and rowing athletes. Material and Methods: 30 male international level and licenced athletes (8 canoeists, 11 kayakers and 11 rowers) are participate to this study; age 20.53±3.23 year, height 181.70±7.90 cm, body weight 78.60±11.22 kg and body mass index 23.73±2.37. The intramuscular oxygen saturation status of athletes were measured using the near infrared spectroscopy technology for 40 second sprint and 4 minutes tests performed on the land with branch-specific ergometers. Data collecting processes were recorded as 5 minute rest, 40 second sprint, 10 minute rest, 4 minute distance performances and 5 minute recovery, in total nearly 25 minute. Results: Comparing the intramuscular oxygen saturation values of canoe, kayak and rowing athletes, no significant difference was found in the decrement rate to minimum, during 40 second sprint for biceps brachii and vastus lateralis (p>0.05). As we compare to 4 minute performances, a significant difference was found in both muscle groups for all three branches. It was seen that the difference was between rowing and canoe athletes, and lower intramuscular oxygen saturation values were found in canoeists compared to rowers (p<0.05). Conclusion: The results show that intramuscular oxygen saturation it can be said that many factors effect such as differences between branches, posture position, cardiovascular differences between athletes, using of body parts, and number of stroke per minute.

A study on the measurement method of a two-component fluid proportion infrared spectrum based on HDC-PSAM

When traditional infrared spectroscopy technology is used for the measurement and quantitative analysis of fluid, due to the complex band composition of the infrared spectrum, weak absorption intensity and spectral signal, the measurement can easily be disturbed, thus increasing the error. This makes it difficult to achieve the requirements for the accurate analysis of fluids. To solve this problem, this paper uses self-developed experimental apparatus and, using water-based drilling fluid and diesel oil, proposes a new method for quantitative research of two-component proportional fluids based on hybrid dilated convolution-pyramid self-attention modules (HDC-PSAMs). A large number of experiments are carried out on water-based drilling fluids, diesel and water mixtures, and a large number of spectral experimental data are preprocessed using moving window smoothing and normalization methods. HDC and PSAMs are used to extract effective features, and are combined with a partial least squares algorithm to model fluid mixtures to achieve quantification of the fluid mixtures. The experimental results show that this method can reduce the influence of disturbance during measurement, has higher accuracy and stability, and effectively improves the measurement accuracy of infrared spectroscopy for quantitative analysis of fluids. This method also provides strong technical support for the analysis of other fluids and the measurement of fluid mixtures using infrared spectroscopy technology.

The Atmospheric Vertical Detection of Large Area Regions Based on Interference Signal Denoising of Weighted Adaptive Kalman Filter.

In comparison with traditional space infrared spectroscopy technology, the interference signals of a large focal plane array (FPA) can be used to obtain spectra over a larger area range and rapidly achieve large-scale coverage of hyperspectral remote sensing. However, the low signal-to-noise ratio of the interference signals limits the application of spectral data, especially when atmospheric detection occurs in the long-wavelength infrared (LWIR) band. In this paper, we construct an LWIR hyperspectral system of a Fourier transform spectrometer composed of a HgCdTe photovoltaic IR FPA and a Michelson interferometer. The LWIR interference signals are obtained by a high-frequency oversampling technique. We use the Kalman filter (KF) and its improved weighted adaptive Kalman filter (WAKF) to reduce the noise of multiple measured data of each pixel. The effect of overshoot and ringing artifacts on the objective signals is reduced by the WAKF. The applicability is studied by the interference signals from the different sampling frequencies and different pixels. The effectiveness is also verified by comparing the spectra of denoised interferograms with the reference spectrum. The experimental results show that the WAKF algorithm has excellent noise suppression, and the standard deviation of the interferogram can be reduced by 39.50% compared with that of KF. The WAKF is more advantageous in improving the signal-to-noise ratio of the interferogram and spectra. The results indicate that our system can be applied to atmospheric vertical detection and hyperspectral remote sensing over large area ranges because our denoised technique is suitable for large LWIR FPA.

Prediction of mango firmness by near infrared spectroscopy tandem with machine learning

The firmness of the mango fruit is one of the internal physical properties that can show its quality. Unfortunately, non-destructive methods to measure this are not yet available. In the current study, we develop a calibration model using near infrared spectroscopy to predict the physical properties (firmness) of the mango cultivar Arumanis (Mangifera indica cv. Arumanis) via machine learning. Spectral data were acquired using the fourier transform near-infrared (FTNIR) benchtop with a wavelength range of 1000 to 2500 nm. Multivariate spectra analysis based on machine learning, including principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), was utilized and compared to estimate the firmness of fresh mangos. The results obtained show that the prediction of machine learning by PLSR is better than that of SVMR and PCR for the prediction of mango firmness. The coefficient correlation of calibration (rc) and validation (rcv), the root means square error of calibration (RMSE-C) and validation (RMSE-CV), and the ratio of prediction to deviation (RPD) were 0.941, 0.382 kgf, 0.920, 0.472 kgf, and 2.556, respectively. The general results satisfactorily indicate that near infrared spectroscopy technology integrated with an appropriate machine learning algorithm has optimistic results in determining the firmness of mango non-destructively.

Prediction of mango firmness by near infrared spectroscopy tandem with machine learning

The firmness of the mango fruit is one of the internal physical properties that can show its quality. Unfortunately, non-destructive methods to measure this are not yet available. In the current study, we develop a calibration model using near infrared spectroscopy to predict the physical properties (firmness) of the mango cultivar Arumanis (Mangifera indica cv. Arumanis) via machine learning. Spectral data were acquired using the fourier transform near-infrared (FTNIR) benchtop with a wavelength range of 1000 to 2500 nm. Multivariate spectra analysis based on machine learning, including principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), was utilized and compared to estimate the firmness of fresh mangos. The results obtained show that the prediction of machine learning by PLSR is better than that of SVMR and PCR for the prediction of mango firmness. The coefficient correlation of calibration (rc) and validation (rcv), the root means square error of calibration (RMSE-C) and validation (RMSE-CV), and the ratio of prediction to deviation (RPD) were 0.941, 0.382 kgf, 0.920, 0.472 kgf, and 2.556, respectively. The general results satisfactorily indicate that near infrared spectroscopy technology integrated with an appropriate machine learning algorithm has optimistic results in determining the firmness of mango non-destructively.